Script to reproduce years based on a model trained with random points¶
Importing¶
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import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.metrics import root_mean_squared_error as rmse
from tqdm import tqdm
import dill
import random
import salishsea_tools.viz_tools as sa_vi
Datasets Preparation¶
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def datasets_preparation(dataset, dataset2):
drivers = np.stack([np.ravel(dataset['Temperature_(0m-15m)']),
np.ravel(dataset['Temperature_(15m-100m)']),
np.ravel(dataset['Salinity_(0m-15m)']),
np.ravel(dataset['Salinity_(15m-100m)']),
np.ravel(dataset2['Summation_of_solar_radiation']),
np.ravel(dataset2['Mean_wind_speed']),
np.ravel(dataset2['Mean_air_temperature'])
])
indx = np.where(~np.isnan(drivers).any(axis=0))
drivers = drivers[:,indx[0]]
diat = np.ravel(dataset['Diatom'])
diat = diat[indx[0]]
return(drivers, diat, indx)
Regressor¶
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def regressor (inputs, targets):
inputs = inputs.transpose()
# Regressor
X_train, _, y_train, _ = train_test_split(inputs, targets, train_size=0.35)
model = MLPRegressor(hidden_layer_sizes=300)
model = make_pipeline(StandardScaler(), model)
regr = BaggingRegressor(model, n_estimators=24, n_jobs=-1).fit(X_train, y_train)
return (regr)
Regressor 2¶
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def regressor2 (inputs, targets, variable_name):
inputs2 = inputs.transpose()
outputs_test = regr.predict(inputs2)
m = scatter_plot(targets, outputs_test, variable_name)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor 3¶
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def regressor3 (inputs, targets):
inputs2 = inputs.transpose()
outputs_test = regr.predict(inputs2)
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs_test, deg=1)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor 4¶
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def regressor4 (inputs, targets, variable_name):
inputs2 = inputs.transpose()
outputs = regr.predict(inputs2)
# Post processing
indx2 = np.full((len(diat_i.y)*len(diat_i.x)),np.nan)
indx2[indx[0]] = outputs
model = np.reshape(indx2,(len(diat_i.y),len(diat_i.x)))
m = scatter_plot(targets, outputs, variable_name + str(dates[i].date()))
# Preparation of the dataarray
model = xr.DataArray(model,
coords = {'y': diat_i.y, 'x': diat_i.x},
dims = ['y','x'],
attrs=dict( long_name = variable_name + "Concentration",
units="mmol m-2"),)
plotting3(targets, model, diat_i, variable_name)
Printing¶
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def printing (targets, outputs, m):
print ('The amount of data points is', outputs.size)
print ('The slope of the best fitting line is ', np.round(m,3))
print ('The correlation coefficient is:', np.round(np.corrcoef(targets, outputs)[0][1],3))
print (' The mean square error is:', rmse(targets,outputs))
Scatter Plot¶
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def scatter_plot(targets, outputs, variable_name):
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs, deg=1)
printing(targets, outputs, m)
fig, ax = plt.subplots(2, figsize=(5,10), layout='constrained')
ax[0].scatter(targets,outputs, alpha = 0.2, s = 10)
lims = [np.min([ax[0].get_xlim(), ax[0].get_ylim()]),
np.max([ax[0].get_xlim(), ax[0].get_ylim()])]
# plot fitted y = m*x + b
ax[0].axline(xy1=(0, b), slope=m, color='r')
ax[0].set_xlabel('targets')
ax[0].set_ylabel('outputs')
ax[0].set_xlim(lims)
ax[0].set_ylim(lims)
ax[0].set_aspect('equal')
ax[0].plot(lims, lims,linestyle = '--',color = 'k')
h = ax[1].hist2d(targets,outputs, bins=100, cmap='jet',
range=[lims,lims], cmin=0.1, norm='log')
ax[1].plot(lims, lims,linestyle = '--',color = 'k')
# plot fitted y = m*x + b
ax[1].axline(xy1=(0, b), slope=m, color='r')
ax[1].set_xlabel('targets')
ax[1].set_ylabel('outputs')
ax[1].set_aspect('equal')
fig.colorbar(h[3],ax=ax[1], location='bottom')
fig.suptitle(variable_name)
plt.show()
return (m)
Plotting¶
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def plotting(variable, name):
plt.plot(years,variable, marker = '.', linestyle = '')
plt.xlabel('Years')
plt.ylabel(name)
plt.show()
Plotting 2¶
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def plotting2(variable,title):
fig, ax = plt.subplots()
scatter= ax.scatter(dates,variable, marker='.', c=pd.DatetimeIndex(dates).month)
ax.legend(handles=scatter.legend_elements()[0], labels=['February','March','April'])
fig.suptitle('Daily ' + title + ' (15 Feb - 30 Apr)')
fig.show()
Plotting 3¶
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def plotting3(targets, model, variable, variable_name):
fig, ax = plt.subplots(2,2, figsize = (10,15))
cmap = plt.get_cmap('cubehelix')
cmap.set_bad('gray')
variable.plot(ax=ax[0,0], cmap=cmap, vmin = targets.min(), vmax =targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
model.plot(ax=ax[0,1], cmap=cmap, vmin = targets.min(), vmax = targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
((variable-model) / variable * 100).plot(ax=ax[1,0], cmap=cmap, cbar_kwargs={'label': variable_name + ' Concentration [percentage]'})
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=0.95,
top=0.95,
wspace=0.35,
hspace=0.35)
sa_vi.set_aspect(ax[0,0])
sa_vi.set_aspect(ax[0,1])
sa_vi.set_aspect(ax[1,0])
ax[0,0].title.set_text(variable_name + ' (targets)')
ax[0,1].title.set_text(variable_name + ' (outputs)')
ax[1,0].title.set_text('targets - outputs')
ax[1,1].axis('off')
fig.suptitle(str(dates[i].date()))
plt.show()
Training (Random Points)¶
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ds = xr.open_dataset('/data/ibougoudis/MOAD/files/integrated_model_var_old.nc')
ds2 = xr.open_dataset('/data/ibougoudis/MOAD/files/external_inputs.nc')
ds = ds.isel(time_counter = (np.arange(0, len(ds.time_counter),2)),
y=(np.arange(ds.y[0], ds.y[-1], 5)),
x=(np.arange(ds.x[0], ds.x[-1], 5)))
ds2 = ds2.isel(time_counter = (np.arange(0, len(ds2.time_counter),2)),
y=(np.arange(ds2.y[0], ds2.y[-1], 5)),
x=(np.arange(ds2.x[0], ds2.x[-1], 5)))
dates = pd.DatetimeIndex(ds['time_counter'].values)
drivers, diat, _ = datasets_preparation(ds, ds2)
regr = regressor(drivers, diat)
Other Years (Anually)¶
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years = range (2007,2024)
r_all = []
rms_all = []
slope_all = []
for year in tqdm(range (2007,2024)):
dataset = ds.sel(time_counter=str(year))
dataset2 = ds2.sel(time_counter=str(year))
drivers, diat, _ = datasets_preparation(dataset, dataset2)
r, rms, m = regressor2(drivers, diat, 'Diatom ' + str(year))
r_all.append(r)
rms_all.append(rms)
slope_all.append(m)
plotting(np.transpose(r_all), 'Correlation Coefficient')
plotting(np.transpose(rms_all), 'Root Mean Square Error')
plotting (np.transpose(slope_all), 'Slope of the best fitting line')
0%| | 0/17 [00:00<?, ?it/s]
The amount of data points is 70794 The slope of the best fitting line is 0.503 The correlation coefficient is: 0.747 The mean square error is: 0.10649864509588611
6%|▌ | 1/17 [00:02<00:44, 2.81s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.532 The correlation coefficient is: 0.724 The mean square error is: 0.10102927170359083
12%|█▏ | 2/17 [00:05<00:41, 2.75s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.532 The correlation coefficient is: 0.781 The mean square error is: 0.12813719987335395
18%|█▊ | 3/17 [00:08<00:38, 2.72s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.448 The correlation coefficient is: 0.691 The mean square error is: 0.10684976373474256
24%|██▎ | 4/17 [00:10<00:35, 2.73s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.628 The correlation coefficient is: 0.818 The mean square error is: 0.0906291753594586
29%|██▉ | 5/17 [00:13<00:32, 2.71s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.628 The correlation coefficient is: 0.813 The mean square error is: 0.09328594886579103
35%|███▌ | 6/17 [00:16<00:30, 2.80s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.464 The correlation coefficient is: 0.741 The mean square error is: 0.12456309360510479
41%|████ | 7/17 [00:19<00:27, 2.79s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.486 The correlation coefficient is: 0.669 The mean square error is: 0.10852362152763363
47%|████▋ | 8/17 [00:22<00:24, 2.75s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.34 The correlation coefficient is: 0.593 The mean square error is: 0.12318730882135746
53%|█████▎ | 9/17 [00:24<00:21, 2.74s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.569 The correlation coefficient is: 0.782 The mean square error is: 0.10561173278635788
59%|█████▉ | 10/17 [00:27<00:19, 2.74s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.565 The correlation coefficient is: 0.729 The mean square error is: 0.09246033293861043
65%|██████▍ | 11/17 [00:30<00:16, 2.71s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.399 The correlation coefficient is: 0.614 The mean square error is: 0.12562946303795774
71%|███████ | 12/17 [00:32<00:13, 2.72s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.483 The correlation coefficient is: 0.677 The mean square error is: 0.12760229583111865
76%|███████▋ | 13/17 [00:35<00:10, 2.70s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.412 The correlation coefficient is: 0.711 The mean square error is: 0.14823774192575895
82%|████████▏ | 14/17 [00:38<00:08, 2.76s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.598 The correlation coefficient is: 0.803 The mean square error is: 0.10411793777685434
88%|████████▊ | 15/17 [00:41<00:05, 2.76s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.541 The correlation coefficient is: 0.716 The mean square error is: 0.10253862783319384
94%|█████████▍| 16/17 [00:43<00:02, 2.72s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.395 The correlation coefficient is: 0.621 The mean square error is: 0.13177333893755458
100%|██████████| 17/17 [00:46<00:00, 2.74s/it]
Other Years (Daily)¶
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r_all2 = np.array([])
rms_all2 = np.array([])
slope_all2 = np.array([])
for i in tqdm(range (0, len(ds.time_counter))):
dataset = ds.isel(time_counter=i)
dataset2 = ds2.isel(time_counter=i)
drivers, diat, _ = datasets_preparation(dataset, dataset2)
r, rms, m = regressor3(drivers, diat)
r_all2 = np.append(r_all2,r)
rms_all2 = np.append(rms_all2,rms)
slope_all2 = np.append(slope_all2,m)
plotting2(r_all2, 'Correlation Coefficients')
plotting2(rms_all2, 'Root Mean Square Errors')
plotting2(slope_all2, 'Slope of the best fitting line')
100%|██████████| 640/640 [00:39<00:00, 16.10it/s]
Daily Maps¶
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maps = random.sample(range(0,len(ds.time_counter)),10)
for i in tqdm(maps):
dataset = ds.isel(time_counter=i)
dataset2 = ds2.isel(time_counter=i)
drivers, diat, indx = datasets_preparation(dataset, dataset2)
diat_i = dataset['Diatom']
regressor4(drivers, diat, 'Diatom ')
0%| | 0/10 [00:00<?, ?it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.86 The correlation coefficient is: 0.827 The mean square error is: 0.03942991549087954
10%|█ | 1/10 [00:00<00:08, 1.09it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.253 The correlation coefficient is: 0.661 The mean square error is: 0.17289962397879405
20%|██ | 2/10 [00:02<00:08, 1.04s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.491 The correlation coefficient is: 0.654 The mean square error is: 0.10885201560907953
30%|███ | 3/10 [00:02<00:06, 1.04it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.382 The correlation coefficient is: 0.535 The mean square error is: 0.13195135003817646
40%|████ | 4/10 [00:03<00:05, 1.07it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.287 The correlation coefficient is: 0.722 The mean square error is: 0.24203411696630844
50%|█████ | 5/10 [00:04<00:04, 1.09it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.544 The correlation coefficient is: 0.719 The mean square error is: 0.07337546118645188
60%|██████ | 6/10 [00:05<00:03, 1.02it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.298 The correlation coefficient is: 0.685 The mean square error is: 0.16388002486556366
70%|███████ | 7/10 [00:06<00:02, 1.06it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.307 The correlation coefficient is: 0.42 The mean square error is: 0.06857933792721058
80%|████████ | 8/10 [00:07<00:01, 1.10it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.184 The correlation coefficient is: 0.409 The mean square error is: 0.1614388369543772
90%|█████████ | 9/10 [00:08<00:00, 1.13it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.497 The correlation coefficient is: 0.585 The mean square error is: 0.07726601441727111
100%|██████████| 10/10 [00:09<00:00, 1.06it/s] 100%|██████████| 10/10 [00:09<00:00, 1.06it/s]
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